1. UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization
Jaegul Choo1*, Changhyun Lee1, Chandan K. Reddy2, and Haesun Park1
1Georgia Institute of Technology, 2Wayne State University *

2. Intro: Topic Modeling Document 1 Document 2 Document 3 Document 4
brain
evolve
dna
genetic
gene
nerve
neuron
life
organism

3. Topic: a distribution over keywords
Intro: Topic Modeling
Document 1
Document 2
Document 3
Document 4
Topic 1
Topic 2
Topic 3
Topic: a distribution over keywords
brain
evolve
dna
genetic
gene
nerve
neuron
life
organism

4. Intro: Topic Modeling Document : a distribution over topic
Topic: a distribution over keywords
brain
evolve
dna
genetic
gene
nerve
neuron
life
organism

5. Latent Dirichlet Allocation (LDA) in Visual Analytics
LDA has been widely used in visual analytics.
TIARA [Wei et al. KDD10], iVisClustering [Lee et al. EuroVis12], ParallelTopics [Dou et al. VAST12], TopicViz [Eisenstein et al. CHI-WIP12], …
*Image courtesy of original papers.

6. Doc-induced topic creation Keyword-induced topic creation
Overview of Our Work
Proposes nonnegative matrix factorization (NMF) for topic modeling.
Highlights advantages of NMF over LDA in visual analytics.
Presents UTOPIAN, an NMF-based interactive topic modeling system.
Topic merging
Topic splitting
Doc-induced topic creation
Keyword-induced topic creation

7. What is Nonnegative Matrix Factorization?

8. Nonnegative Matrix Factorization (NMF)
Lower-rank approximation with nonnegativity constraints
Why nonnegativity?
Easy interpretation and semantically meaningful output
Algorithm
Alternating nonnegativity-constrained least squares [Kim et al., 2008] H ~ =
min || A – WH ||F
W>=0, H>=0 A W
Mention document vector

9. NMF as Topic Modeling ~ = Document : a distribution over topicW W H H ~ = A
Document 1
Document 2
Document 3
Document 4
Document :
a distribution over topic
Topic 1
Topic 2
Topic 3
Topic: a distribution over keywords
brain
evolve
dna
genetic
gene
nerve
neuron
life
organism

10. Why NMF in Visual Analytics?

11. Advantages of NMF in Visual Analytics
Reliable algorithmic behaviors
Flexible support for user interactions

12. NMF vs. LDA Consistency from Multiple Runs
Documents’ topical membership changes among 10 runs
InfoVis/VAST paper data set
20 newsgroup data set

13. NMF vs. LDA Empirical Convergence
Documents’ topical membership changes between iterations
InfoVis/VAST paper data set
48 seconds
10 minutes
NMF
LDA

14. NMF vs. LDA Topic Summary (Top Keywords)
InfoVis/VAST paper data set
Topics are more consistent in NMF than in LDA.
Topic quality is comparable between NMF and LDA.
Topic 1
Topic 2
Topic 3
Topic 4
Topic 5
Topic 6
Topic 7
NMF
Run #1
visualization
design
information
user
analysis
system
graph
layout
visual
analytics
data
sets
color
weaving
Run #2
LDA
documents
similarities
knowledge
edge
query
collaborative
social
tree
measures
multivariate
animation
dimensions
treemap
analysts
scatterplot
spatial
text
multidimensional, high
aggregation

15. Advantages of NMF in Visual Analytics
Reliable algorithmic behaviors
Flexible support for user interactions

16. Weakly Supervised NMF [Choo et al., DMKD, accepted with rev.]
min ||A – WH ||F2 + α||(W – Wr)MW ||F2 + β||MH(H – DHHr) ||F2
W>=0, H>=0
Wr, Hr : reference matrices for W and H
MW, MH : diagonal matrices for weighting/masking columns/rows of W and H
Provides flexible yet intuitive means for user interaction.
Maintains the same computational complexity as original NMF.

17. UTOPIAN: User-Driven Topic Modeling Based on Interactive NMF
Topic merging
Topic splitting
Doc-induced topic creation
Keyword-induced topic creation

18. Doc-induced topic creation Keyword-induced topic creation
UTOPIAN Overview
Supervised t-distributed stochastic neighbor embedding (t-SNE)
User interactions supported
Keyword refinement
Topic merging/splitting
Keyword-/document-induced
topic creation
Real-time interaction via
PIVE (Per-Iteration
Visualization Environment)
Topic merging
Topic splitting
Doc-induced topic creation
Keyword-induced topic creation
Just like In-Spire, documents are represented as dots, and their colors represent their topic cluster membership.

19. Supervised t-SNE Original t-SNE
Documents are often too noisy to work with.
Supervised t-SNE
d(xi, xj) ← α•d(xi, xj) if xi and xj belongs to the same topic cluster.

20. PIVE (Per-Iteration Visualization Environment) for Real-time Interaction [Choo et al., under revision]
Standard approach
PIVE approach

21. Demo Video http://tinyurl.com/UTOPIAN2013

22. Usage Scenario: Hyundai Genesis Review Data
Initial result
After interaction

23. Summary
Presented UTOPIAN, a User-Driven Topic Modeling based on Interactive NMF.
Highlighted the advantages of NMF over LDA in visual analytics.
Reliable algorithmic behaviors
Consistency from multiple runs
Early empirical convergence
Flexible support for user interactions
Keyword refinement
Topic merging/splitting
Keyword-/document-induced topic creation

24. More in the paper & On-going Work
A general taxonomy of user interactions with computational methods
Keyword-based vs. document-based
Template-based vs. from-scratch-based
Algorithmic details about supported user interactions
Implementation details
More usage scenarios
On-going Work
Scaling up the system with parallel distributed NMF

25. Thank you! http://tinyurl.com/UTOPIAN2013
Jaegul Choo
Topic merging
Topic splitting
Doc-induced topic creation
Keyword-induced topic creation
For more details, please find me at ‘Meet the Candidate’ A601+ A602, 6PM today